DSpace
 

DSpace at IIT Bombay >
IITB Publications >
Article >

Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/14370

Title: GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion
Authors: VINH, NX
CHETTY, M
COPPEL, R
WANGIKAR, PP
Issue Date: 2011
Publisher: OXFORD UNIV PRESS
Citation: BIOINFORMATICS,27(19)2765-2766
Abstract: Motivation: Dynamic Bayesian networks (DBN) are widely applied in modeling various biological networks including the gene regulatory network (GRN). Due to the NP-hard nature of learning static Bayesian network structure, most methods for learning DBN also employ either local search such as hill climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing. Results: This article presents GlobalMIT, a toolbox for learning the globally optimal DBN structure from gene expression data. We propose using a recently introduced information theoretic-based scoring metric named mutual information test (MIT). With MIT, the task of learning the globally optimal DBN is efficiently achieved in polynomial time.
URI: http://dx.doi.org/10.1093/bioinformatics/btr457
http://dspace.library.iitb.ac.in/jspui/handle/100/14370
ISSN: 1367-4803
Appears in Collections:Article

Files in This Item:

There are no files associated with this item.

View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback